# L40S vs H100: which GPU fits your workload and budget?

> L40S vs H100: 48 GB GDDR6 at 864 GB/s for $890 a month against 80 GB HBM3 at 3.35 TB/s for $2,190. See which models fit and which jobs each GPU suits best.

Source: https://overgpu.com/compare/l40s-vs-h100  
Updated: 2026-10-22  
Publisher: OverGPU (https://overgpu.com/)

**In short:** For small and medium models, media and rendering, the L40S does the job at $890 a month; for training, larger models and bandwidth-hungry LLM serving, the H100 is worth its $2,190.

## L40S vs H100 specifications

| Specification | NVIDIA L40S | NVIDIA H100 SXM |
| --- | --- | --- |
| Architecture | Ada Lovelace | Hopper |
| GPU memory | 48 GB GDDR6 ECC | 80 GB HBM3 |
| Memory bandwidth | 864 GB/s | 3.35 TB/s |
| FP4 | — | — |
| FP8 | 733 TFLOPS | 1,979 TFLOPS |
| BF16 / FP16 | 362 TFLOPS | 989 TFLOPS |
| GPU interconnect | PCIe Gen4 x16 | NVLink 4 · 900 GB/s |
| Form factor | Dual-slot PCIe card | SXM5 module · HGX H100 |
| Max power | 350 W | 700 W |

## Monthly prices at OverGPU

| Configuration | Type | GPU memory | CPU | RAM | Storage | Network | Price per month (USD) | Per GPU-hour | Availability |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| [NVIDIA L40S](https://overgpu.com/gpus/nvidia-l40s) | Single GPU | 48 GB GDDR6 | 16 vCPU | 128 GB | 1 TB NVMe | 10 Gbps | $890 | $1.22 | In stock, delivered in under 10 minutes after payment |
| [NVIDIA H100 SXM](https://overgpu.com/gpus/nvidia-h100) | Single GPU | 80 GB HBM3 | 24 vCPU | 225 GB | 2 TB NVMe | 25 Gbps | $2,190 | $3.00 | In stock, delivered in under 10 minutes after payment |
| [8× NVIDIA HGX H100](https://overgpu.com/gpus/nvidia-h100) | 8-GPU server | 8 × 80 GB HBM3 | 2× Xeon 8480+ · 112 cores | 2 TB DDR5 | 8× 3.84 TB NVMe | 8× 400G ConnectX-7 | $16,900 | $2.89 | In stock, delivered in under 10 minutes after payment |

Per GPU-hour = monthly price ÷ number of GPUs ÷ 730 hours, shown only to compare with hourly offers: every plan is billed as one flat monthly price.

## Choose the L40S if…

- Your models fit in 48 GB, such as 7B to 14B models in BF16 or up to about 30B in FP8.
- You run image or video generation, transcoding or GPU rendering that needs RT cores or video encoders.
- You want more GPU memory per dollar and can scale out across independent servers.

## Choose the H100 if…

- You train or fully fine-tune models, or serve models larger than 48 GB.
- Your LLM serving is limited by memory bandwidth, where 3.35 TB/s against 864 GB/s matters.
- You need several GPUs working on one model over NVLink in an 8-GPU server.

## Overview

The **L40S vs H100** comparison is about matching the GPU to the job. The L40S is an Ada Lovelace card with 48 GB of GDDR6 at 864 GB/s on PCIe Gen4, designed for both AI and graphics workloads. The H100 SXM is a Hopper compute accelerator with 80 GB of HBM3 at 3.35 TB/s and NVLink. At OverGPU the L40S costs $890 a month and the H100 $2,190, so the right pick can save you $1,300 a month per GPU, and the wrong one can leave you with a card that does not suit the job.

## L40S vs H100: the short answer

Pick the [L40S](https://overgpu.com/gpus/nvidia-l40s) for inference on models that fit in 48 GB, for image and video generation, and for rendering and transcoding. Pick the [H100](https://overgpu.com/gpus/nvidia-h100) for training, full fine-tuning, models above 48 GB and LLM serving where bandwidth sets the pace. The H100 has no RT cores and no hardware video encoders, so for rendering and video work the L40S is the better fit despite its lower price.

## Memory: what fits on each GPU

Weights take roughly 2 bytes per parameter in BF16, 1 in FP8 and 0.5 in 4-bit formats, and both GPUs support FP8 in hardware. Free memory below is before KV cache and runtime overhead, so keep 10–30% of it in reserve.

| Model and precision | Weights (approx.) | L40S, 48 GB | H100, 80 GB |
| --- | --- | --- | --- |
| 8B in BF16 | 16 GB | Yes, about 32 GB free | Yes, about 64 GB free |
| 14B in BF16 | 28 GB | Yes, about 20 GB free | Yes, about 52 GB free |
| 32B in FP8 | 32 GB | Yes, about 16 GB free | Yes, about 48 GB free |
| 32B in BF16 | 64 GB | No | Tight, about 16 GB free |
| 70B in 4-bit | 35 GB | Tight, about 13 GB free | Yes, about 45 GB free |
| 70B in FP8 | 70 GB | No | Tight, about 10 GB free |

Free memory also sets concurrency. Take a 14B model with grouped-query attention in a common layout (48 layers, 8 KV heads of dimension 128): its KV cache needs about 0.2 MB per token in BF16. After runtime overhead, roughly 14 GB remain for the cache on the L40S, about 70,000 tokens or eight conversations of 8K tokens at once. The H100 keeps roughly 44 GB, about 220,000 tokens or some 27 such conversations.

Image and video models are usually smaller than LLMs, but high resolutions and long clips need memory for activations. Image models such as SDXL and Flux-class models fit in 48 GB with their text encoders, while the largest video models can need more. If you need more than 48 GB without moving to Hopper, the RTX PRO 6000 with 96 GB of GDDR7 at 1.6 TB/s costs $1,390 a month; see [RTX PRO 6000 vs H100](https://overgpu.com/compare/rtx-pro-6000-vs-h100). The [VRAM calculator](https://overgpu.com/tools/vram-calculator) runs these numbers for your model.

## Bandwidth and compute: why the H100 costs more

The H100 has about 3.9 times the L40S's memory bandwidth (3.35 TB/s against 864 GB/s) and, per NVIDIA, about 2.7 times its dense FP8 and BF16 throughput (1,979 against 733 TFLOPS in FP8, 989 against 362 TFLOPS in BF16). For LLM serving the bandwidth gap matters most, because generating each token reads the weights and KV cache from memory. A 14B model in BF16 runs on both GPUs, but on the H100 each decoding step reads memory at close to four times the rate, which leaves room for many more concurrent users per GPU.

Diffusion models for images and video are more compute-bound, so there the relevant gap is closer to the compute ratio than to the bandwidth ratio. Per $1,000 of monthly spend, the two GPUs compare like this:

- **Memory:** About 54 GB on the L40S against 37 GB on the H100.
- **Bandwidth:** About 0.97 TB/s against 1.53 TB/s.
- **Dense FP8, vendor-listed:** About 824 against 904 TFLOPS.

Of the two, the L40S buys more memory per dollar, the H100 more bandwidth per dollar, and compute per dollar is close.

## Media, rendering and video: where the L40S wins

GPU renderers that use NVIDIA's ray-tracing hardware need RT cores, and video pipelines that encode on the GPU need NVENC. The L40S has both, and its Ada-generation encoders handle AV1 as well as H.264 and HEVC; the H100 has neither RT cores nor NVENC, because it is built as a compute accelerator. For a render node, a transcoding service or a generative video pipeline that ends in encoded output, the L40S is the right choice, and two L40S servers cost $1,780 a month, less than one H100. See [rendering and VFX](https://overgpu.com/solutions/rendering-vfx) and [generative media](https://overgpu.com/solutions/generative-media) for suggested configurations. For heavier scenes, the 96 GB RTX PRO 6000 is compared in [RTX PRO 6000 vs L40S](https://overgpu.com/compare/rtx-pro-6000-vs-l40s).

## Training, fine-tuning and multi-GPU

For training, the H100 is in a different class. HBM3 keeps its tensor cores fed, 80 GB holds larger models and batches, and in an 8-GPU server NVLink 4 links the GPUs at 900 GB/s each. The L40S communicates over PCIe Gen4 with no NVLink, and we offer it as a single-GPU server. It handles LoRA or QLoRA on 7B to 14B models and small experiments well. QLoRA on a 70B model, about 35 GB of 4-bit weights, can squeeze into 48 GB with small batches, but an H100 runs it with far more headroom. Full fine-tuning with Adam needs roughly 16–20 bytes per parameter before activations, which caps a 48 GB card at about 2–3B parameters, against about 4–5B on one H100 and far more on an 8× H100 server at $16,900 a month.

## Cost per month and how to decide

One L40S costs $890 a month, about $1.22 per GPU-hour over 730 hours, with 16 vCPUs, 128 GB of RAM and 1 TB of NVMe. One H100 costs $2,190, about $3.00 per GPU-hour, with 24 vCPUs, 225 GB of RAM and 2 TB of NVMe.

A worked example: you serve a 14B model in BF16 to steady traffic. Two L40S servers ($1,780) give you two independent replicas and redundancy if one needs maintenance. One H100 ($2,190) gives you a single replica with more bandwidth than both L40S cards combined (3.35 TB/s against 1.73 TB/s). If redundancy matters most, take the pair; if per-replica capacity and response time matter most, take the H100. For serving setups, see [dedicated inference servers](https://overgpu.com/solutions/inference).

1. Rendering, transcoding or video output: L40S.
2. Model plus KV cache under about 40 GB: L40S, unless you need high concurrency on one replica.
3. Between 40 and 70 GB, or bandwidth-bound serving: H100.
4. Training, full fine-tuning or multi-GPU models: H100, often as an 8-GPU server.

If you are unsure, start small. Terms are monthly and you can cancel any month without a fee, so you can run real traffic on an L40S and move to an H100 if the numbers call for it.

## Frequently asked questions

### Is the L40S good for LLM inference?

Yes, for models that fit in 48 GB, such as 7B to 14B models in BF16 or up to about 30B in FP8. For 70B-class models or high-concurrency serving, the H100's 80 GB and 3.35 TB/s of bandwidth are the better fit.

### Can the H100 be used for rendering?

It is not designed for it, because the H100 has no RT cores and no hardware video encoders. For GPU rendering and video work, the L40S or the RTX PRO 6000 is the better choice.

### Does the L40S support FP8?

Yes. As an Ada Lovelace GPU it has FP8 tensor cores, with 733 TFLOPS of dense FP8 listed by NVIDIA, against 1,979 TFLOPS for the H100.

### Can I link several L40S GPUs together?

The L40S has no NVLink and we rent it as a single-GPU server, so it suits workloads that scale out as independent replicas. Private networking between your servers is available on request, but for one model spread across several GPUs an 8-GPU H100 server with NVLink is the better tool.

### How much does each cost per month?

The L40S is $890 a month and the H100 is $2,190, about $1.22 and $3.00 per GPU-hour over a 730-hour month. Both are billed monthly in advance with no setup fee, and you can cancel any month.

## Related pages

- [Rent a dedicated NVIDIA L40S for $890 per month](https://overgpu.com/gpus/nvidia-l40s): A dedicated NVIDIA L40S 48 GB for $890/month, about $1.22 per GPU-hour at 24/7, for inference, image generation and rendering. Paid in crypto.
- [Rent NVIDIA H100 SXM servers from $2,190 per month](https://overgpu.com/gpus/nvidia-h100): Rent a dedicated NVIDIA H100 SXM for $2,190/month (about $3.00 per GPU-hour at 24/7) or an 8-GPU HGX H100 for $16,900. Flat price, delivered in under 10 minutes.
- [RTX PRO 6000 vs L40S: is 96 GB worth the step up?](https://overgpu.com/compare/rtx-pro-6000-vs-l40s): 96 GB vs 48 GB, FP4 vs FP8, $1,390 vs $890 a month. See which card your work needs.
- [RTX PRO 6000 vs H100: more memory or more bandwidth?](https://overgpu.com/compare/rtx-pro-6000-vs-h100): 96 GB of GDDR7 for $1,390 a month or 80 GB of HBM3 with NVLink for $2,190. See which suits 70B inference or multi-GPU training.
- [GPU rendering servers for Blender, Redshift and Octane](https://overgpu.com/solutions/rendering-vfx): With 96 GB RTX PRO 6000 Blackwell GPUs, up to 768 GB of VRAM per server, booked monthly for crunch periods. See sizing and setup.
- [GPU servers for LLM inference, running 24/7 at a flat price](https://overgpu.com/solutions/inference): KV cache sizing, FP8 and FP4, and 20–50 TB of bandwidth included. See the sizing math.